The count of pending cases has shown an exponential rise across nations
(e.g., with more than 10 million pending cases in India alone). The main issue
lies in the fact that the number of cases submitted to the law system is far
greater than the available number of legal professionals present in a country.
Given this worldwide context, the utilization of AI technology has gained
paramount importance to enhance the efficiency and speed of legal procedures.
In this study we partcularly focus on helping legal professionals in the
process of analyzing a legal case. Our specific investigation delves into
harnessing the generative capabilities of open-sourced large language models to
create arguments derived from the facts present in legal cases. Experimental
results show that the generated arguments from the best performing method have
on average 63% overlap with the benchmark set gold standard annotations.

通过利用开源大型语言模型的生成能力，本研究着重于帮助法律专业人员分析法律案件，实验结果显示最佳方法生成的论点与基准集黄金标准标注平均重叠度达到 63%。

法律事实的自动论证生成

Automated Argument Generation from Legal Facts

In the Indian court system, pending cases have long been a problem. There are
more than 4 crore cases outstanding. Manually summarising hundreds of documents
is a time-consuming and tedious task for legal stakeholders. Many
state-of-the-art models for text summarization have emerged as machine learning
has progressed. Domain-independent models don't do well with legal texts, and
fine-tuning those models for the Indian Legal System is problematic due to a
lack of publicly available datasets. To improve the performance of
domain-independent models, the authors have proposed a methodology for
normalising legal texts in the Indian context. The authors experimented with
two state-of-the-art domain-independent models for legal text summarization,
namely BART and PEGASUS. BART and PEGASUS are put through their paces in terms
of extractive and abstractive summarization to understand the effectiveness of
the text normalisation approach. Summarised texts are evaluated by domain
experts on multiple parameters and using ROUGE metrics. It shows the proposed
text normalisation approach is effective in legal texts with domain-independent
models.

该研究提出了一种在印度法律文本中标准化文本的方法，并测试了两种最先进的专业无关模型用于法律文本摘要。通过领域专家的评估和 ROUGE 指标的使用，结果表明所提出的文本标准化方法在专业无关模型中的效果很好。